Advanced Machine Learning Operations
In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.
The content was developed for participants with these skills/knowledge/abilities:
The user should have intermediate-level knowledge of traditional machine learning concepts, development, and the use of Python and Git for ML projects.
It is recommended that the user has intermediate-level experience with Python.
Outline
Review of MLOps
Streamlining Development to Deployment
Continuous Workflows for Machine Learning Operations
Streamlining MLOps
Streamlining MLOps with Databricks
Testing Strategies with Databricks
Automate Comprehensive Testing
Model Rollout Strategies with Databricks
Model Quality and Lakehouse Monitoring
Introduction to Monitoring
Lakehouse Monitoring
Streamlining Multiple Environment Deployments - DABsBuild ML assets as CodeCourse Summary and Next Steps
Upcoming Public Classes
Date | Time | Language | Price |
---|---|---|---|
Feb 14 | 09 AM - 01 PM (America/New_York) | English | $750.00 |
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